Examples for intrachromosomal mRNA co‐regulation patches

Slides:

Advertisements

Similar presentations

GFP‐Sed5p localization defect in sgt2Δ.

Advertisements

Tuning the response of the DPI

DNase‐HS sites are main independent determinants of DNA replication timing Simulations based on genome sequence features (GC content, CpG islands), or.

Principles of using neural networks for predicting molecular traits from DNA sequence Principles of using neural networks for predicting molecular traits.

Network topology reflects target gene expression profile and function.

MRNA coexpression of neighbouring genes is driven by chromatin fluctuations and regulatory interference mRNA coexpression of neighbouring genes is driven.

Inferred dynamic model of transcriptional regulatory network.

Summary of all the reactions considered in the SmartCell simulation of the three circuits. Summary of all the reactions considered in the SmartCell simulation.

Most promoters preserve their relative activity levels across conditions. Most promoters preserve their relative activity levels across conditions. (A)

Analysis of in silico flux changes along the exponential growth phase: (A) In silico flux changes from 24 to 36 h, from 36 to 48 h, and from 48 to 60 h.

Motif detectability corresponds to the phylogenetic profile of the cognate transcription factor. Motif detectability corresponds to the phylogenetic profile.

Two competing models for Rab domain identity generation and endosome conversion. Two competing models for Rab domain identity generation and endosome conversion.

Functional analysis of duplicate pair CIK1–VIK1 (A) Genetic interaction profile similarity. Functional analysis of duplicate pair CIK1–VIK1 (A) Genetic.

Proteins with hotspot mutations are often in the interaction networks with known cancer driver proteins Proteins with hotspot mutations are often in the.

Validation of the CCE associations by mass spectrometry.

Mononucleotide models describe sequence‐to‐shape relationships well

Comparative analysis of RNA and protein profiles.

Highly correlating interaction profiles can predict functional similarity Highly correlating interaction profiles can predict functional similarity AROC.

Buffering of non‐functional mRNA co‐regulation likely is a passive process Buffering of non‐functional mRNA co‐regulation likely is a passive process Percentage.

Luminex phosphorylation measurements are reproducible and have broad dynamic range Individual difference in insulin‐induced phosphorylation of ERK measured.

Correlating protein to mRNA and proteins per mRNA ratios

Spatial proximity of genes affects mRNA but not protein regulation

Coupling immunophenotypes to Drop‐seq data

Network derived from large‐scale fractionation predicts 48 protein complexes and communities Network derived from large‐scale fractionation predicts 48.

Hierarchical structure in the yeast transcription regulatory network.

Genomic profiling of fitness in periodic salt stress

Modules with causal links to ALS are associated with inflammation and enriched with TDP‐43 protein–protein interactions (PPIs)‏ Modules with causal links.

Heat maps showing global relative growth phenotype and comparison between measured and predicted values. Heat maps showing global relative growth phenotype.

Identification of essential genome features.

Alignment time for Clustal Omega (red), MAFFT (blue), MUSCLE (green) and Kalign (purple) against the number of sequences of HomFam test sets. Alignment.

Physical EGFR interactome generation and core network proteins identification. Physical EGFR interactome generation and core network proteins identification.

The transcript profiles in the three human cell lines based on RNA sequencing (RNA‐seq). The transcript profiles in the three human cell lines based on.

Homologous recombination deficiency scores correlate with drug‐induced cell death in primary ovarian cancer cells Homologous recombination deficiency scores.

The limiting, degrading entity hypothesis

Properties of proteins and residues with frequent hotspot mutations

Characterization of Factor 5 (oxidative stress response factor) in the CLL data Characterization of Factor 5 (oxidative stress response factor) in the.

Shared components define the ‘ancient’ phagosome.

Francis Crick's central dogma of biology revolves around the transcription of mRNA from DNA, the translation of proteins from mRNA, and the degradation.

Antisense transcription, measurement, and correction

Building and using sequence‐expression‐activity maps (SEAMAPs) AThe RBS Library Calculator designs a synthetic RBS library to efficiently search a multi‐dimensional.

Evaluation of assay performance with positive and negative binary reference sets Evaluation of assay performance with positive and negative binary reference.

Allele‐specific expression at single‐cell resolution

Two‐way unsupervised uncentered unnormalized hierarchical clustering using a Pearson's correlation of the phenotypic profiles of 4281 nonessential genes.

Transcriptomic and epigenetic changes associated with Factor 1 in the scMT data Transcriptomic and epigenetic changes associated with Factor 1 in the scMT.

Feedback signalling through TP53‐CDKN1A‐GADD45A‐MAPK14‐GRB2‐TGFBRII‐TGFβ induces ROS production and maintains DDR. (A–C) MRC5 cells were transfected with.

Comparison of proteomics and RNA‐Seq data.

Characteristics of tissue‐specific co‐expression networks (CNs)‏

DominoEffect R package

Determination of mRNA synthesis and decay rates.

Submodules of the light yellow module define distinct interneuron classes with common developmental origins. Submodules of the light yellow module define.

An integrated NHR network.

Design and optimization of the computational model.

Subnetworks enriched for the hallmarks of cancer.

RSLs enable internal replicate and lineage dropout analyses

Example transcript and protein patterns.

Reproducibility of DeathPro drug screens

Antisense‐mediated regulation of SUR7.

A multitiered approach to characterize transcriptome structure.

Melting behavior of protein complexes

Characterising dermis expansion and gene expression changes during mouse development (related to Fig 1)‏ Characterising dermis expansion and gene expression.

Nedd4 proteins. Nedd4 proteins. (A) Schematic representation of rNedd4‐1, hNedd4‐1 and hNedd4‐2 (not to scale), with % amino‐acid identity between them.

Antisense expression associates with larger gene expression variability. Antisense expression associates with larger gene expression variability. (A–D)

Drug sensitivity described by LD50 is similar in cells cultured in 2D or as organoids Drug sensitivity described by LD50 is similar in cells cultured in.

Global and focused views of human interaction map.

Rare and abundant codons.

Single‐cell RT–qPCR data

The transcriptional behavior of GREB1 changes with estrogen dose and exhibits considerable cell‐to‐cell variation (see also Fig EV2 and Movie EV2)‏ The.

Intestinal differentiation is coupled with global rewiring of gene expression Intestinal differentiation is coupled with global rewiring of gene expression.

Topologies, synthetic implementations and expression profiles of the networks studied Topologies, synthetic implementations and expression profiles of.

Fig. 2 Overview of transcriptional mutagenesis in yeast.

Presentation transcript:

Examples for intrachromosomal mRNA co‐regulation patches Examples for intrachromosomal mRNA co‐regulation patches Chromosome 16 has several prominent patches of genes with strongly co‐regulated mRNAs, similar to chromosome 11 shown in Fig 1D. These genes are not co‐regulated on the protein level but interact with each other in the 3D structure of the genome. Note that the top right region displays increased Hi‐C contact frequencies, but this is not reflected in the co‐regulation map.Chromosome 12 is an example where only weak co‐regulated patches are visible, and these align only partially with Hi‐C contact patches.Chromosome 19 is a unique case, being a small but very gene‐dense chromosome that is characterised by general co‐regulation of most of its genes on the mRNA level, and a generally elevated Hi‐C contact frequency, with no impact on protein co‐regulation.Data information: mRNA and protein co‐regulation shown as Pearson's correlation coefficient (PCC). Dashed rectangles are shown for orientation. Numbers in grey box show the Pearson correlation between the Hi‐C map and mRNA (blue) or protein (red) co‐regulation maps. Georg Kustatscher et al. Mol Syst Biol 2017;13:937 © as stated in the article, figure or figure legend